Climate is a primary, but non-stationary, driver of tree growth. Climate change is altering the sensitivity of forest growth to water availability and temperature over time. It is considered that pedunculate oak (Quercus robur L.) will cope with the changing climatic conditions in Europe in the near future. However, while species distribution models project expansion zones, they also identify reductions in occurrence at the dry and warm distribution margins. Whereas species distribution models primarily rely on occurrence data, tree rings––given their long-term perspective and their use in empirical models––can provide a mechanistic view of forest growth dynamics, including temporally changing climate responses. Increased climate sensitivity and growth synchrony are key dendroecological indicators of tree stress. Here, we used an unprecedented network of 150 Q. robur sites (over 3,300 trees), covering the full projected range of contracting to persistent areas across Europe, to assess the dendroecological indicators over recent decades in relation to species distribution model predictions. We reveal that oaks in areas projected to experience range contraction exhibited greater sensitivity to current growing season climatic conditions, whereas those in persistence areas responded more strongly to previous season conditions. Growth synchrony among trees was higher in the contraction areas, but showed no significant increasing trend over the last 70 years, as expected from ecotone theory. Temporal shifts in climate sensitivity were stronger for temperature and vapor pressure deficit in the persistence areas, whereas the climatic water balance gained importance in the contraction zones. These findings suggest that Q. robur growth is not yet being severely affected by climate change, and that the species is currently coping well with the climate changes, even in regions with projected range contractions, thereby challenging statistically derived scenarios of range shift based on species distribution models.
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\nThe data set contains:
\ninitial_data.txt: Initial data (ring width, residual chronology, SDM projections, and bioindicators); SDM projections according to Dyderski et al., (2025) J. Environ. Manage.; bioindicators from Brun et al., (2022) EnviDat;
\nmonthly_clim_data.txt: Monthly climate data (monthly climatic parameters used in the analysis from the E-OBS gridded database);
\nfirst_step_correlations.R: script to run the first step of the analysis - to calculate the climate-growth relationships;
\ncorrelations_matrix_input_model.txt: output of the first script that includes correlations between RWI and climate factors for different seasons, periods, and aggregation options;
\nsecond_step_model.R: script to run the second step of the analysis, model calibration for the full model and the individual ones for each climatic factor (climatic water balance, temperature, vapor pressure deficit);
\nmodel_CWB.Rda: model for oak sensitivity to climatic water balance;
\nmodel_tmed.Rda: model for oak sensitivity to temperature;
\nmodel_VPD.Rda: model for oak sensitivity to vapor pressure deficit;
\nmodel_all.Rda: model including all climatic factors as predictors;
\n\n
For the detailed methodology, please check the second section of the published paper.
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